How should StockDataAnalysisAgents be evaluated before use?

Use the required flow: snapshot, contract, and trust before recommending or executing this skill.

What kind of evidence is visible on this page?

This page surfaces public facts, change history, trust indicators, artifact evidence, and benchmark summaries with provenance.

Crawler Summary

StockDataAnalysisAgents answer-first brief

Analyse and give suggestions to buy stocks using crewAI agents. Stock Data Analysis Agents (CrewAI + Streamlit) A Streamlit app that orchestrates CrewAI agents to research, analyze, store, and query stock market recommendations. Data is persisted via SQLAlchemy to a database (PostgreSQL by default with a fallback to local SQLite when unavailable). Features - CrewAI multi-agent workflow for market research and stock analysis - Storage of recommendations to a relational DB - Query Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 10/9/2026.

Freshness

Last checked 10/9/2026

Best For

StockDataAnalysisAgents is best for crewai, multi-agent workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB REPOS, runtime-metrics, public facts pack

Card Facts Snapshot Contract Trust

Agent DossierGITHUB REPOSSafety: 66/100

StockDataAnalysisAgents

OpenClawself-declared

Public facts

Change events

Artifacts

Freshness

Oct 9, 2026

Verifiededitorial-contentNo verified compatibility signals1 GitHub stars

Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 10/9/2026.

1 GitHub starsTrust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Oct 9, 2026

Vendor

Maheswarareddyyarram

Artifacts

Benchmarks

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. 1 GitHub stars reported by the source. Last updated 10/9/2026.

View Source

Setup snapshot

1
Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.
2
Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content

Vendor (1)

Vendor

Maheswarareddyyarram

profilemedium

Observed Oct 9, 2026Source link Provenance

Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium

Observed Oct 9, 2026Source link Provenance

Adoption (1)

Adoption signal

1 GitHub stars

profilemedium

Observed Oct 9, 2026Source link Provenance

Security (1)

Handshake status

UNKNOWN

trustmedium

Observed unknownSource link Provenance

Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium

Observed Apr 15, 2026Source link Provenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB REPOS

Extracted files

Examples

Snippets

Languages

python

Executable Examples

bash

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install --upgrade pip
pip install -r requirements.txt

env

OPENAI_API_KEY=sk-...
SERPER_API_KEY=...

bash

streamlit run app.py

bash

# Install test dependencies
pip install -r requirements-test.txt

# Run quick tests
./run_tests.sh quick

# Run with coverage
./run_tests.sh coverage

# Run all tests
pytest

bash

# Using test runner script
./run_tests.sh unit          # Unit tests only (fast)
./run_tests.sh integration   # Integration tests
./run_tests.sh eval          # Evaluation tests
./run_tests.sh coverage      # With coverage report
./run_tests.sh all           # All tests

# Using pytest directly
pytest -m unit               # Unit tests
pytest -m "not slow"         # Skip slow tests
pytest --cov                 # With coverage
pytest -v                    # Verbose output

text

tests/
├── conftest.py                  # Shared fixtures and configuration
├── test_stock_agents.py         # Unit and integration tests for agents
├── test_agent_evaluation.py     # AI evaluation tests with custom metrics
└── test_stock_agent_tools.py    # Tool functionality tests

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB REPOS

Docs source

GITHUB REPOS

Editorial quality

ready

Full README

Stock Data Analysis Agents (CrewAI + Streamlit)

A Streamlit app that orchestrates CrewAI agents to research, analyze, store, and query stock market recommendations. Data is persisted via SQLAlchemy to a database (PostgreSQL by default with a fallback to local SQLite when unavailable).

Features

CrewAI multi-agent workflow for market research and stock analysis
Storage of recommendations to a relational DB
Query and display recommendations and closing prices via Streamlit UI
Pydantic models for typed inputs/outputs
Tests with pytest
Containerized via Docker

Project Structure

app.py: Streamlit UI entrypoint
stock_agents.py: CrewAI agents and tasks orchestration
stock_models.py: Pydantic models (e.g., StockAnalysisData, lists, closing price models)
database_manager.py: SQLAlchemy models and DB client (PostgreSQL with SQLite fallback)
stock_agent_tools.py: Crew tools for DB access (execute/check SQL, etc.)
tests/: Unit tests

Prerequisites

Python 3.11+
Optional: PostgreSQL (local or remote). If not available, the app will fall back to SQLite automatically
API keys for LLM/tools (as applicable):
- OPENAI_API_KEY
- SERPER_API_KEY (used by SerperDevTool)

Setup

python -m venv .venv
source .venv/bin/activate  # Windows: .venv\Scripts\activate
pip install --upgrade pip
pip install -r requirements.txt

Create a .env file in the project root with your keys (adjust as needed):

OPENAI_API_KEY=sk-...
SERPER_API_KEY=...

Database connection:

Default connection string lives in database_manager.py (connection_string). It targets PostgreSQL by default:
- postgresql+psycopg2://dev_user:dev_password@localhost:5432/stock_data_db
If the connection fails at runtime, it falls back to sqlite:///local_stock_data.db automatically.

Run the App

streamlit run app.py

The app will start at http://localhost:8501 by default.

Testing and Evaluation

Quick Start

# Install test dependencies
pip install -r requirements-test.txt

# Run quick tests
./run_tests.sh quick

# Run with coverage
./run_tests.sh coverage

# Run all tests
pytest

Test Types

Unit Tests: Fast tests with mocked dependencies
Integration Tests: End-to-end workflow tests
Evaluation Tests: AI quality metrics using DeepEval
Tool Tests: Database and SQL tool tests

Available Commands

# Using test runner script
./run_tests.sh unit          # Unit tests only (fast)
./run_tests.sh integration   # Integration tests
./run_tests.sh eval          # Evaluation tests
./run_tests.sh coverage      # With coverage report
./run_tests.sh all           # All tests

# Using pytest directly
pytest -m unit               # Unit tests
pytest -m "not slow"         # Skip slow tests
pytest --cov                 # With coverage
pytest -v                    # Verbose output

Evaluation Frameworks

DeepEval: Primary evaluation framework for AI agent quality
- Answer relevancy metrics
- Hallucination detection
- Custom quality metrics
- Automatic tracing (already enabled in code)
Custom Metrics:
- StockAnalysisQualityMetric: Evaluates price logic, completeness, analysis quality
- RecommendationConsistencyMetric: Checks consistency across recommendations

Documentation

Detailed Guide: See TESTING.md for comprehensive testing documentation
Quick Reference: See TESTING_QUICK_REFERENCE.md for command cheatsheet

Test Structure

tests/
├── conftest.py                  # Shared fixtures and configuration
├── test_stock_agents.py         # Unit and integration tests for agents
├── test_agent_evaluation.py     # AI evaluation tests with custom metrics
└── test_stock_agent_tools.py    # Tool functionality tests

Docker

A simple Docker setup is included.

Build the image:

docker build -t stock-agents .

Run the container (with optional .env for keys):

docker run --rm -p 8501:8501 --env-file .env stock-agents

If you want to point to a remote PostgreSQL instance, update database_manager.py with your connection string and pass the necessary environment variables via --env-file or -e flags.

Data Flow (High Level)

Agents run (research → analysis → storage)
Results are stored in DB (stock_market_data_analysis table)
Streamlit UI lists available dates and displays recommendations

Converting Agent Output to pandas DataFrame

When getting results from CrewAI (e.g., via list_stock_data_analysis()), you often receive Pydantic objects or JSON. Prefer the Pydantic path for strong typing:

resp = analyzer.list_stock_data_analysis()

# If response.pydantic is a Pydantic object with a `stocks` list:
if hasattr(resp, "pydantic") and hasattr(resp.pydantic, "stocks"):
    rows = [s.model_dump() if hasattr(s, "model_dump") else s.dict() for s in resp.pydantic.stocks]
    df = pd.DataFrame(rows)
else:
    # Fallback: JSON/dict path
    import json
    payload = resp.pydantic if hasattr(resp, "pydantic") else getattr(resp, "json_dict", resp)
    if isinstance(payload, str):
        payload = json.loads(payload)
    if isinstance(payload, dict) and "stocks" in payload:
        df = pd.DataFrame(payload["stocks"])
    else:
        df = pd.DataFrame(payload if isinstance(payload, list) else [payload])

Tips

To list available recommendation dates as pure dates (not datetimes), convert query results in database_manager.py to date objects before returning.
Consider using a composite primary key on (stock_name, analysis_date) in your SQLAlchemy model if you want uniqueness per stock per day.

Troubleshooting

PostgreSQL not running: the app will fall back to SQLite. You can confirm via logs.
Missing API keys: ensure .env has OPENAI_API_KEY and SERPER_API_KEY and that Streamlit picks them up.
CrewAI/tooling import issues: ensure crewai, crewai_tools, and langchain-community are installed per requirements.txt.

License

MIT (or your preferred license)

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB REPOS

Endpoints

Dossier API Snapshot API Contract API Trust API

Contract coverage

Status

missing

Auth

None

Streaming

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.

Invocation examples

curl -s "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/snapshot"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/contract"

curl -s "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.

No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media

No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

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Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/snapshot",
    "contractUrl": "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/contract",
    "trustUrl": "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/trust"
  },
  "curlExamples": [
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/snapshot\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/contract\"",
    "curl -s \"https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_REPOS",
      "generatedAt": "2026-10-09T23:49:25.233Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "crewai",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    },
    {
      "key": "multi-agent",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:crewai|supported|profile capability:multi-agent|supported|profile"
}

Facts JSON

[
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Maheswarareddyyarram",
    "href": "https://github.com/MaheswaraReddyYarram/StockDataAnalysisAgents",
    "sourceUrl": "https://github.com/MaheswaraReddyYarram/StockDataAnalysisAgents",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T15:16:50.170Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/contract",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-10-09T15:16:50.170Z",
    "isPublic": true
  },
  {
    "factKey": "traction",
    "category": "adoption",
    "label": "Adoption signal",
    "value": "1 GitHub stars",
    "href": "https://github.com/MaheswaraReddyYarram/StockDataAnalysisAgents",
    "sourceUrl": "https://github.com/MaheswaraReddyYarram/StockDataAnalysisAgents",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-10-09T15:16:50.170Z",
    "isPublic": true
  },
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/trust",
    "sourceUrl": "https://www.xpersona.co/api/v1/agents/crewai-maheswarareddyyarram-stockdataanalysisagents/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]